Enhancing Long-Term Vegetation Monitoring in Australia: A New Approach for Harmonising and Gap-Filling AVHRR and MODIS NDVI Article Swipe
Chad Burton
,
Sami W. Rifai
,
Luigi J. Renzullo
,
Albert I. J. M. van Dijk
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/essd-2024-89
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/essd-2024-89
Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global AVHRR NDVI products for their suitability for long-term vegetation monitoring in Australia. Comparisons with MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce inter-annual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA’s Climate Data Record AVHRR NDVI to MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) and another independent of climate data (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at a spatial resolution of 0.05°, exhibit strong correlation and low relative errors compared to MODIS NDVI, accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with observations. Finally, we provide a complete 41-year dataset where gap filled AusENDVI from January 1982 to February 2000 is seamlessly joined with MODIS NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia’s annual maximum NDVI revealed increasing values across most of the continent. Moreover, shifts in the timing of annual peak NDVI are identified, underscoring the dataset's potential to address crucial questions regarding changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on terrestrial carbon and water cycles, and provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/essd-2024-89
- OA Status
- gold
- Cited By
- 1
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394611536
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4394611536Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/essd-2024-89Digital Object Identifier
- Title
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Enhancing Long-Term Vegetation Monitoring in Australia: A New Approach for Harmonising and Gap-Filling AVHRR and MODIS NDVIWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-09Full publication date if available
- Authors
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Chad Burton, Sami W. Rifai, Luigi J. Renzullo, Albert I. J. M. van DijkList of authors in order
- Landing page
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https://doi.org/10.5194/essd-2024-89Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/essd-2024-89Direct OA link when available
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Normalized Difference Vegetation Index, Term (time), Vegetation (pathology), Environmental science, Remote sensing, Vegetation Index, Geography, Geology, Climate change, Medicine, Oceanography, Physics, Quantum mechanics, PathologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Data | 99 |
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| abstract_inverted_index.also | 193 |
| abstract_inverted_index.data | 126, 139 |
| abstract_inverted_index.era. | 183 |
| abstract_inverted_index.from | 227, 239 |
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| abstract_inverted_index.most | 258 |
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| abstract_inverted_index.used | 293 |
| abstract_inverted_index.with | 52, 214, 236 |
| abstract_inverted_index.2022. | 244 |
| abstract_inverted_index.AVHRR | 39, 101 |
| abstract_inverted_index.MODIS | 53, 104, 160, 237 |
| abstract_inverted_index.March | 240 |
| abstract_inverted_index.NDVI, | 161 |
| abstract_inverted_index.These | 141 |
| abstract_inverted_index.cover | 203 |
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| abstract_inverted_index.their | 43 |
| abstract_inverted_index.these | 86 |
| abstract_inverted_index.using | 109 |
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| abstract_inverted_index.where | 223 |
| abstract_inverted_index.which | 22 |
| abstract_inverted_index.woody | 202 |
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| abstract_inverted_index.annual | 251, 268 |
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| abstract_inverted_index.errors | 157 |
| abstract_inverted_index.failed | 68 |
| abstract_inverted_index.filled | 225 |
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| abstract_inverted_index.shifts | 263 |
| abstract_inverted_index.strong | 29, 152 |
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| abstract_inverted_index.40-year | 246 |
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| abstract_inverted_index.January | 228 |
| abstract_inverted_index.Landsat | 81 |
| abstract_inverted_index.MCD43A4 | 105 |
| abstract_inverted_index.address | 85, 278 |
| abstract_inverted_index.another | 135 |
| abstract_inverted_index.change, | 18 |
| abstract_inverted_index.change. | 322 |
| abstract_inverted_index.climate | 31, 125, 138 |
| abstract_inverted_index.closely | 172 |
| abstract_inverted_index.crucial | 279 |
| abstract_inverted_index.cycles, | 308 |
| abstract_inverted_index.dataset | 210, 222, 290 |
| abstract_inverted_index.densely | 60, 167 |
| abstract_inverted_index.diverse | 26 |
| abstract_inverted_index.drivers | 319 |
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| abstract_inverted_index.impacts | 302 |
| abstract_inverted_index.maximum | 252 |
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| abstract_inverted_index.spatial | 147 |
| abstract_inverted_index.(Burton, | 331 |
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| abstract_inverted_index.December | 243 |
| abstract_inverted_index.February | 231 |
| abstract_inverted_index.Finally, | 216 |
| abstract_inverted_index.NOAA’s | 97 |
| abstract_inverted_index.approach | 92 |
| abstract_inverted_index.assessed | 66 |
| abstract_inverted_index.changing | 282, 297 |
| abstract_inverted_index.climate, | 197 |
| abstract_inverted_index.compared | 158 |
| abstract_inverted_index.complete | 220 |
| abstract_inverted_index.datasets | 3, 120 |
| abstract_inverted_index.decision | 112 |
| abstract_inverted_index.drivers. | 287 |
| abstract_inverted_index.dynamics | 299 |
| abstract_inverted_index.ensemble | 114 |
| abstract_inverted_index.evaluate | 35 |
| abstract_inverted_index.existing | 37 |
| abstract_inverted_index.fraction | 204 |
| abstract_inverted_index.products | 41, 67 |
| abstract_inverted_index.provides | 310 |
| abstract_inverted_index.regions. | 62, 169 |
| abstract_inverted_index.relative | 156 |
| abstract_inverted_index.reliable | 2, 185, 312 |
| abstract_inverted_index.research | 316 |
| abstract_inverted_index.revealed | 254 |
| abstract_inverted_index.seasonal | 164 |
| abstract_inverted_index.spanning | 143 |
| abstract_inverted_index.studying | 295 |
| abstract_inverted_index.versions | 117 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Analysing | 245 |
| abstract_inverted_index.Australia | 21, 108 |
| abstract_inverted_index.Empirical | 132 |
| abstract_inverted_index.Moreover, | 63, 262 |
| abstract_inverted_index.agreement | 213 |
| abstract_inverted_index.available | 328 |
| abstract_inverted_index.condition | 7, 179 |
| abstract_inverted_index.dataset's | 275 |
| abstract_inverted_index.datasets, | 142 |
| abstract_inverted_index.developed | 194 |
| abstract_inverted_index.ecosystem | 13 |
| abstract_inverted_index.essential | 9 |
| abstract_inverted_index.excellent | 212 |
| abstract_inverted_index.highlight | 55 |
| abstract_inverted_index.indicated | 79 |
| abstract_inverted_index.leverages | 196 |
| abstract_inverted_index.long-term | 46 |
| abstract_inverted_index.per-pixel | 247 |
| abstract_inverted_index.phenology | 284 |
| abstract_inverted_index.potential | 276 |
| abstract_inverted_index.pre-MODIS | 76, 182 |
| abstract_inverted_index.questions | 280 |
| abstract_inverted_index.regarding | 281 |
| abstract_inverted_index.replicate | 173 |
| abstract_inverted_index.reproduce | 71 |
| abstract_inverted_index.responses | 14 |
| abstract_inverted_index.resulting | 207 |
| abstract_inverted_index.synthetic | 208 |
| abstract_inverted_index.vegetated | 61, 168 |
| abstract_inverted_index.Australia. | 50 |
| abstract_inverted_index.Australian | 131 |
| abstract_inverted_index.Long-term, | 1 |
| abstract_inverted_index.accurately | 162 |
| abstract_inverted_index.adequately | 70 |
| abstract_inverted_index.anomalies. | 83 |
| abstract_inverted_index.continent. | 261 |
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| abstract_inverted_index.downstream | 301 |
| abstract_inverted_index.ecosystems | 27 |
| abstract_inverted_index.foundation | 313 |
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| abstract_inverted_index.monitoring | 48 |
| abstract_inverted_index.predictors | 129 |
| abstract_inverted_index.resolution | 148 |
| abstract_inverted_index.seamlessly | 234 |
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| abstract_inverted_index.1982–2013 | 144 |
| abstract_inverted_index.Australia's | 296 |
| abstract_inverted_index.Comparisons | 51 |
| abstract_inverted_index.atmospheric | 198 |
| abstract_inverted_index.calibrating | 94 |
| abstract_inverted_index.correlation | 153 |
| abstract_inverted_index.gap-filling | 188 |
| abstract_inverted_index.harmonising | 96 |
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| abstract_inverted_index.interannual | 30, 175 |
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| abstract_inverted_index.reproducing | 163 |
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| abstract_inverted_index.terrestrial | 12, 304 |
| abstract_inverted_index.variability | 73, 176 |
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| corresponding_author_ids | https://openalex.org/A5025318421, https://openalex.org/A5026540150, https://openalex.org/A5026818785, https://openalex.org/A5082726122 |
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| corresponding_institution_ids | https://openalex.org/I118347636, https://openalex.org/I1286406326, https://openalex.org/I5681781 |
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| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.68689764 |
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| citation_normalized_percentile.is_in_top_10_percent | False |